An information theoretic approach to reverse engineering of regulatory gene networks from time-course data

Pietro Zoppoli, Sandro Morganella, Michele Ceccarelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Several methods have been developed to infer gene networks from steady-state data, much less literature is produced about time-course data, so the development of algorithms to infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. In particular, we show how the ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) algorithm can be used for gene regulatory network inference in the case of time-course expression profiles. The resulting method is called TimeDelay-ARACNE. It just tries to extract dependencies between two genes at different time delays, providing a measure of these dependencies in terms of mutual information. The basic idea of the proposed algorithm is to detect time-delayed dependencies between the expression profiles by assuming as underlying probabilistic model a stationary Markov Random Field. Less informative dependencies are filtered out using an auto calculated threshold, retaining most reliable connections. TimeDelay-ARACNE can infer small local networks of time regulated gene-gene interactions detecting their versus and also discovering cyclic interactions also when only a medium-small number of measurements are available. We test the algorithm both on synthetic networks and on microarray expression profiles. Microarray measurements are concerning part of S. cerevisiae cell cycle and E. coli SOS pathways. Our results are compared with the ones of two previously published algorithms: Dynamic Bayesian Networks and systems of ODEs, showing that TimeDelay-ARACNE has good accuracy, recall and F-score for the network reconstruction task.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages97-111
Number of pages15
Volume6160 LNBI
DOIs
Publication statusPublished - 26 Aug 2010
Externally publishedYes
Event6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2009 - Genoa, Italy
Duration: 15 Oct 200917 Oct 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6160 LNBI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2009
CountryItaly
CityGenoa
Period15/10/0917/10/09

Fingerprint

Reverse engineering
Reverse Engineering
Gene Regulatory Network
Genes
Time Delay
Cellular Networks
Gene
Gene Networks
Microarray
Microarrays
Time series
Time delay
Dynamic Bayesian Networks
Molecular Biology
Molecular biology
Cell Cycle
Saccharomyces Cerevisiae
Network Algorithms
Information Theory
Mutual Information

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Zoppoli, P., Morganella, S., & Ceccarelli, M. (2010). An information theoretic approach to reverse engineering of regulatory gene networks from time-course data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6160 LNBI, pp. 97-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6160 LNBI). https://doi.org/10.1007/978-3-642-14571-1_8

An information theoretic approach to reverse engineering of regulatory gene networks from time-course data. / Zoppoli, Pietro; Morganella, Sandro; Ceccarelli, Michele.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6160 LNBI 2010. p. 97-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6160 LNBI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zoppoli, P, Morganella, S & Ceccarelli, M 2010, An information theoretic approach to reverse engineering of regulatory gene networks from time-course data. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 6160 LNBI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6160 LNBI, pp. 97-111, 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2009, Genoa, Italy, 15/10/09. https://doi.org/10.1007/978-3-642-14571-1_8
Zoppoli P, Morganella S, Ceccarelli M. An information theoretic approach to reverse engineering of regulatory gene networks from time-course data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6160 LNBI. 2010. p. 97-111. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-14571-1_8
Zoppoli, Pietro ; Morganella, Sandro ; Ceccarelli, Michele. / An information theoretic approach to reverse engineering of regulatory gene networks from time-course data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6160 LNBI 2010. pp. 97-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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